The genetic basis of autoimmunity seen through the lens of T cell functional traits

Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?

-between statisticians and immunologists -> between computational scientists and immunologists?-future studies should consider introducing genetic variation -> mention that this has to be high throughput -The term "tissue-resident" can be used in the last paragraph Ferhat Ay and Sourya Bhattacharyya Reviewer #2 (expert in bioinformatics, functional genomics and genetics of complex diseases): The manuscript, "The genetic basis of autoimmunity seen through the lens of T cell functional traits" by Kaitlyn A. Lagattuta et al., addresses a critical issue in autoimmune disease research: the genetic underpinnings and the role of T cell functional traits in these diseases.The authors have integrated data from genome-wide association studies (GWASs) and functional trait analysis to provide a comprehensive and insightful exploration of this subject matter.Their investigation of T cell functional traits within the context of autoimmunity and the resulting discussion on cell typespecific eQTLs, cdr3QTLs, and the role of T cell states enrich the existing literature in this field.There are a few minor concerns that could be addressed: 1.The manuscript would benefit from a broader discussion on other types of QTLs, such as caQTLs, hQTLs, meQTL,etc.An article by Lara Bossini-Castillo et al., "Immune disease variants modulate gene expression in regulatory CD4+ T cells" (https://doi.org/10.1016/j.xgen.2022.100117),delves into these QTL types and their implications for understanding the regulation of CD4+ T cells.Incorporating insights from this study could enrich your analysis and discussion.2. The manuscript describes several cell state abundance QTLs (csaQTLs).Including a visual representation, such as a figure illustrating a csaQTL, could enhance readers' comprehension of this type of QTL and its implications.3.In the "Future directions" section, consider discussing the identification of causal cell types as a crucial part of the functional interpretation of GWAS data.While numerous computational methods have been developed for this purpose, it remains a challenging and important area of research.4. It would be beneficial for the authors to mention existing important resources, databases, or tools that could aid readers in further understanding the genetic basis of autoimmunity and T cell functional traits.
Reviewer #3 (expert in rheumatology, rheumatoid arthritis disease susceptibility and bioinformatics): Lagattuta et al present a well written, clear, concise but comprehensive overview of functional traits studies in T cells, with a focus on QTLs, and explain how they can be utilised to interpret GWAS findings.This topic is very relevant for scientists working in autoimmune diseases, given the focus on T cells, but it will also be of interest to the wider community in the area of complex traits genetics.The authors do a great job at covering the most recent findings relating to the topic and, importantly, at hinting at the complexity involving molecular traits e.g. the importance of T cell states and the need to investigate patients' samples from inflamed tissues.My suggestion would be to expand the discussion around the apparent lack of co-localization between eQTLs and GWAS variants (this seems very relevant, if the goal is to functionally interpret GWAS variants; is the co-localization better in single cell datasets from patients' tissue?), and perhaps include a comment on recent work by Franke et al regarding co-expression QTLs, which adds to the above-mentioned complexity.

RESPONSE LETTER
We thank the reviewers for providing thoughtful and constructive feedback on the manuscript.In response to their comments, we have made substantial improvements to the manuscripts, which we organize below in a point-by-point response.

Reviewer #1 Comment 1
If allowed in this article format, we suggest that authors use another figure or make a new panel in Figure 1 that shows schematic for other types of QTLs relevant to T cells that are discussed here.This will set up the review nicely.

RESPONSE:
We thank the reviewer for this suggestion.We agree that an additional figure to illustrate other types of QTLs would enhance the comprehensiveness of our manuscript:

RESPONSE:
We thank the reviewer for these detailed recommendations.We have updated the manuscript text accordingly: updated Main text: For example, rs3087243 (reference allele: "G", alternate allele: "A"): an individual can have 0, 1, or 2 copies of the "G" allele.Each additional copy of the "G" allele corresponds to a decrease in the expression of CTLA4 in T cells 4,5 .As an inhibitory receptor, CTLA4 is a vital negative regulator of T cell activation 6 .Since rs3087243 is located within one Mb of the CTLA4 gene body (1.1 kB from the coding sequence), When introducing the SNP rs3087243 and CTLA4: D. Multi-step mechanisms in trans-eQTL: need to provide some potential such mechanisms, maybe 1-2 sentences.The same goes for cis, cis effects are also sufficiently complex that "directly" may not be a good term to refer to them.

RESPONSE:
We thank the reviewer for recommending that we add more detail in our definitions of cis-and trans-eQTL effects.We have updated the manuscript text to include an example mechanism through which a variant could impact expression of a cis-gene and an example mechanism through which a variant could impact expression of a trans-gene: updated Main text: Located near their target genes, cis-eQTLs impact expression often by altering transcription factor binding in a proximal regulatory element, or altering the rate of mRNA degradation.In contrast, trans-eQTLs, which are located farther away from their target genes, can emerge from a wide range of mechanisms.trans-eQTLs may regulate distant gene targets by first altering the expression of a nearby transcription factor (cis-eQTL mediation) 8 .However, trans-eQTLs can also emerge from unusual mechanisms.For example, trans-eQTLs in the major histocompatibility (MHC) locus on chromosome 6 affect the expression of TCR genes on chromosomes 7 and 14 9,10 .Most likely, these trans-eQTLs change the set of antigenic peptides bound and presented by HLA, which in turn shapes the thymic selection of TCRs.

Reviewer #1 Comment 3.1
Page 4, they should replace the term "colocalize" and use overlap/coincide since colocalization now refers to a separate statistical analysis to identify the shared variants jointly associated with two traits.

RESPONSE:
We agree that it is essential to disambiguate overlapping genomic loci from a formal statistical test for colocalization.For this reason, we use the term "coincide" rather than "colocalize" on page 4: However, most risk variants for autoimmune disease do not coincide with the eQTLs identified through bulk RNAseq.
For completeness, we have taken another look at all four uses of the term "colocalize" in the manuscript to ensure that they each refer to a formal statistical test.They do indeed all refer to applications of either coloc (Giambartolomei et al. 2013) or JLIM (Chun et al. 2017).

Reviewer #1 Comment 3.2
Also, the link between the lack of overlapping SNPs and cell-specificity is not clear (page 5, 1st paragraph).

RESPONSE:
We thank the reviewer for pointing out this lack of clarity.We have added text to further explain this connection: updated Main text: However, bulk RNAseq aggregates gene expression across all cells in a given sample.It is possible that eQTLs relevant to disease only occur in pathogenic subsets of cells, and are obscured in bulk RNAseq aggregation.

Reviewer #1 Comment 4
Although the authors mentioned that eQTLs and GWAS SNPs rarely coincide (page 4), they can be in LD as well.It is not clear whether the 25% considers LD or not.Also, 25% is a large number to call "rarely".

RESPONSE:
We thank the reviewer for pointing out this lack of clarity.We have added text to further explain this connection: updated Main text: However, most risk variants for autoimmune disease do not coincide with the eQTLs identified through bulk RNAseq.With a statistical approach that accounts for linkage disequilibrium (LD), only 25% of autoimmune GWAS associations appear to share underlying genetic causes ("statistical colocalization") with eQTL results from bulk RNAseq 14 .

Reviewer #1 Comment 5
Authors should include a broader set of QTL literature and their functional characterization, including some of the references below:

RESPONSE:
We thank the reviewer for providing a broader list of QTL studies.We have incorporated each of these references into manuscript: updated Main text: T reg cells, while highly relevant to immune-mediated disease, constitute a rare cell state (less than 5% of peripheral T cells 18 ).Thus, it will be important to continue focused isolation and analysis of the T reg cell population for eQTL discovery 19  updated Main text: rs7731626 corresponded to a larger increase in IL6ST expression in regulatory T cells (T regs ) compared to non-regulatory T cells (Figure 1a).Evidence that the functional consequences of rs7731626 may be concentrated in T regs could extend to many other autoimmune-associated SNPs, which are known to be enriched in certain naive T reg -specific regulatory regions of the genome 1,2,17 (Ohkura et al.

2020). updated Main text:
There are multiple ways to represent how eQTL effects depend on transcriptional context.We use dimensionality reduction to identify major transcriptional gradients that each approximate a cell state such as T reg .We then identify cell-state-dependent eQTLs, where a genotype's effect on a gene's expression is modulated by the transcriptional gradient 5 .Because transcriptional gradients may be tagged by key marker genes, however, it is sometimes possible to reframe cell-state-dependent eQTLs as co-expression QTLs (co-eQTLs) 20,21  updated Main text: In a similar study, Yazar*, Alquicira-Hernandez*, Wing* et al. 23 applied scRNAseq to PBMCs at steady state, and scanned for eQTLs that colocalized with genetic risk for autoimmune disease.They observed that 68% of the T cell eQTLs colocalizing with disease loci were detected in only one of the T cell states.

Reviewer #1 Comment 6
Page 5, the authors mention that "accounting for T cell states has substantially enhanced disease-relevant eQTL discovery in T cells".It would be great if they provided more specific information on what fraction of these cell-state-specific eQTLs are actually disease-relevant and are missed by bulk RNA-seq.

RESPONSE:
We thank the reviewer for this suggestion.We agree this is one of the central takeaways of the piece, and are happy to provide more details.
updated Main text: Multiple studies have recently estimated that a substantial portion of eQTLs depend on cell state and that this proportion increases when considering autoimmune disease loci.Soskic*, Cano-Gamez* et al. 22 profiled CD4+ T cells over a time course of anti-CD3/anti-CD28 stimulation, and found that 2,265 of 6,407 (35%) eQTLs depended on the activation state of the T cell.They then queried whether each eQTL statistically colocalized with genetic risk for immune-mediated disease.60% of the colocalizing eQTLs were specific to T cell activation, and would have been missed if they did not account for this cell state dependence.In a similar study, Yazar*, Alquicira-Hernandez*, Wing* et al. 23 applied scRNAseq to PBMCs at steady state, and scanned for eQTLs that colocalized with genetic risk for autoimmune disease.They observed that 68% of the T cell eQTLs colocalizing with disease loci were detected in only one of the T cell states.Evidently, accounting for T cell states has substantially enhanced disease-relevant eQTL discovery in T cells.The majority of autoimmune GWAS associations, however, still remain unexplained.

Reviewer #1 Comment 7
Page 6, another type of QTL studied in T cells (by BLUEPRINT and others) is histone modification QTLs.These should be referenced for completeness.

RESPONSE:
We thank the reviewer for this suggestion.

Reviewer #1 Comment 8
Page 7, can a few more sentences be said about morphology QTLs to inform the readers about how such QTLs are derived?

RESPONSE:
We thank the reviewer for this suggestion.This type of QTL is indeed quite different from the others, and warrants a bit more explanation: updated Main text: We and others have identified cell morphology QTLs (cmQTLs) 25

RESPONSE:
We thank the reviewer for these suggestions.We have incorporated a reference to Kim-Hellmuth et al.Science 2020 to motivate cell-type-specific eQTLs: updated Main text: It is possible that eQTLs relevant to disease only occur in pathogenic subsets of cells, and are obscured in bulk RNAseq aggregation.Consistent with this hypothesis, dividing bulk RNAseq tissue samples into constituent cell types by flow cytometry 15 or in silico deconvolution 16 has nominated more eQTLs which are significantly enriched for disease-associated loci.
We have also added a few comments regarding our perspective on the integration of cell-state-specific eQTLs with gene expression modules: updated Main text: Close collaboration between experimental, statistical, and computational scientists will be essential to interpret gene modules suggested by modern scRNAseq datasets in the context of long-established T cell functions.Using these gene modules to represent T cell state may uncover more eQTLs that are context dependent.Ongoing work in our group seeks to define the genetic regulation of these functional modules, and to what extent they colocalize with autoimmune disease risk.

Reviewer #1 Comment 10
Many eQTL / GWAS studies omit the HLA regions from analysis.Authors need to put their comments regarding using HLA region-specific QTLs (page 8).

RESPONSE:
We thank the reviewer for this suggestion.Our laboratory is indeed quite interested in QTL discovery for the MHC region, and we are happy to include pointers in this direction: updated Main text: Due to extreme linkage disequilibrium, the MHC locus is routinely excluded from QTL studies.However, with careful statistical approaches designed to capture the effects of HLA haplotypes 35 , the MHC locus can be robustly investigated for QTLs 36 .

Reviewer #1 Comment 11
In the conclusion, it is not clear how multiple types of QTLs (eQTLs, pQTLs, cdrQTLs) would be jointly analyzed for cell-specificity and disease relevance.Do the authors suggest a simple overlap between them or refer to specific multimodal/multivariate analysis?More pointers would be great for the field.

RESPONSE:
We thank the reviewer for this suggestion.We agree that it is not obvious how to proceed, and have spent some time thinking about the approaches that are emerging.We have added the following paragraph to the Future Directions section: updated Main text: Molecular characterizations of disease-associated loci will continue to gain complexity.Synthesizing results across different types of QTLs presents a new challenge that will soon become critical.For example, how should we interpret a disease-associated locus that appears to regulate the chromatin accessibility of a gene (caQTL) but not the gene's expression (cis-eQTL)?Bossini-Castillo et al. suggest that we have not yet identified the relevant cell state for the cis-eQTL 19 .Another possibility, however, is that the locus is a cis-eQTL; its association test just falls short of statistical significance due to limited power.Statistical methods that boost colocalization power by integrating evidence across multiple -omic layers are starting to emerge.For example, the Bayesian method OPERA 45 identified 58% more genes relevant to complex traits after considering six types of QTLs (e.g.caQTLs, mQTLs, pQTLs) in conjunction with eQTLs.We are eager to see this approach extended to T cell functional traits, with appropriate modeling of cell state dependence.

Reviewer #1 Comment 12
each which -> each of which

RESPONSE:
We thank the reviewer for catching this grammatical error.We have corrected the text accordingly: updated Main text: In a recent demonstration of the power of cQTLs, Nath et al 28 analyzed 11 circulating cytokines, each of which may functionally represent the coordinated activity of thousands of genes.

Reviewer #1 Comment 13
between statisticians and immunologists -> between computational scientists and immunologists?

RESPONSE:
We thank the reviewer for this suggestion.We have expanded the terminology as following: updated Main text: Close collaboration between experimental, statistical, and computational scientists will be essential to interpret gene modules suggested by modern scRNAseq datasets in the context of long-established T cell functions

Reviewer #1 Comment 13
future studies should consider introducing genetic variation -> mention that this has to be high throughput

RESPONSE:
We thank the reviewer for this suggestion.We modified the text accordingly: updated Main text: Future work should extend these genome editing approaches to introduce disease-relevant genetic variants in primary T cells in a high throughput manner, and characterize the molecular traits that result.

Reviewer #1 Comment 14
The term "tissue-resident" can be used in the last paragraph

RESPONSE:
We thank the reviewer for this suggestion.We have modified the text accordingly: updated Main text: Tissue-resident T cells may play a crucial role in the development of autoimmunity.

Reviewer #2 Comment 1
The manuscript would benefit from a broader discussion on other types of QTLs, such as caQTLs, hQTLs, meQTL,etc.An article by Lara Bossini-Castillo et al., "Immune disease variants modulate gene expression in regulatory CD4+ T cells" (https://doi.org/10.1016/j.xgen.2022.100117),delves into these QTL types and their implications for understanding the regulation of CD4+ T cells.Incorporating insights from this study could enrich your analysis and discussion.

RESPONSE:
We thank the reviewer for this reference and suggestion.We have expanded our coverage of QTL types, while keeping the piece focused on T cells and autoimmunity: updated Main text: Other types of QTLs are steadily gaining recognition, such as chromatin accessibility QTLs (caQTLs), histone modification QTLs (hQTLs), DNA methylation QTLs (meQTLs), splicing QTLs (sQTLs), and protein QTLs (pQTLs) (Figure 2).These QTLs are conceptually analogous to eQTLs, with gene expression substituted for some other molecularly-defined trait; particular approaches are comprehensively reviewed elsewhere 24 .
We reviewed Bossini-Castillo et al. with great interest, and incorporated this reference into the manuscript in multiple places: updated Main text: T reg cells, while highly relevant to immune-mediated disease, constitute a rare cell state (less than 5% of peripheral T cells 18 ).Thus, it will be important to continue focused isolation and analysis of the T reg cell population for eQTL discovery 19 (Bossini-Castillo et al. 2022).
updated Main text: For example, how should we interpret a disease-associated locus that appears to regulate the chromatin accessibility of a gene (caQTL) but not the gene's expression (cis-eQTL)?Bossini-Castillo et al. suggest that we have not yet identified the relevant cell state for the cis-eQTL 19 .

Reviewer #2 Comment 2
The manuscript describes several cell state abundance QTLs (csaQTLs).Including a visual representation, such as a figure illustrating a csaQTL, could enhance readers' comprehension of this type of QTL and its implications.

RESPONSE:
We thank the reviewer for this suggestion.We have designed an additional figure that includes a visual depiction of csaQTLs:

Reviewer #2 Comment 3
In the "Future directions" section, consider discussing the identification of causal cell types as a crucial part of the functional interpretation of GWAS data.While numerous computational methods have been developed for this purpose, it remains a challenging and important area of research.

RESPONSE:
We thank the reviewer for this observation.As suggested, we have added some context to the Future Directions section: updated Main text: Building a functional interpretation of a GWAS largely consists of two tasks: linking loci to genes, and identifying critical cell state(s).Identifying disease-critical cell states based on GWAS is a complex challenge, but recent progress has been made with multimodal scATAC-RNAseq 40 .

Reviewer #2 Comment 4
It would be beneficial for the authors to mention existing important resources, databases, or tools that could aid readers in further understanding the genetic basis of autoimmunity and T cell functional traits

RESPONSE:
We thank the reviewer for their insightful suggestion.There is certainly an abundance of tools and databases relevant to this topic, so we have compiled a short list of those we consider most useful.We hope this additional

RESPONSE:
We thank the reviewer for this suggestion.We agree this is one of the main discussion points of the piece, and have expanded our remarks in two places.First, with respect to improved colocalization in single cell analyses accounting for cell state: updated Main text: Multiple studies have recently estimated that a substantial portion of eQTLs depend on cell state and that this proportion increases when considering autoimmune disease loci.Soskic*, Cano-Gamez* et al. 22 profiled CD4+ T cells over a time course of anti-CD3/anti-CD28 stimulation, and found that 2,265 of 6,407 (35%) eQTLs depended on the activation state of the T cell.They then queried whether each eQTL statistically colocalized with genetic risk for immune-mediated disease.60% of the colocalizing eQTLs were specific to T cell activation, and would have been missed if they did not account for this cell state dependence.In a similar study, Yazar*, Alquicira-Hernandez*, Wing* et al. 23 applied scRNAseq to PBMCs at steady state, and scanned for eQTLs that colocalized with genetic risk for autoimmune disease.They observed that 68% of the T cells eQTLs colocalizing with disease were detected in only one of the T cell states.Evidently, accounting for T cell states has substantially enhanced disease-relevant eQTL discovery in T cells.The majority of autoimmune GWAS associations, however, still remain unexplained.
In response to the review's point regarding patient tissue, we've added to our Future Directions section: updated Main text: Studies examining T cells from inflamed tissues have identified disease-relevant T cell states that were not previously appreciated: for example, IL17+ CD8 T cells in UC 46 and GZMK+ T cells in RA 58 .Single-cell profiling of Systemic Lupus Erythematosus (SLE) patient samples recently identified specific regulation of ORMDL3 at a previously difficult to annotate SLE locus.Hence additional emphasis on collecting samples from patients with autoimmune disease and directly sampling tissue sites of inflammation is critical.

Reviewer #3 Comment 2
and perhaps include a comment on recent work by Franke et al regarding co-expression QTLs, which adds to the above-mentioned complexity.

RESPONSE:
We thank the reviewer for this suggestion.We too have been intrigued by Franke et al.'s formulation of gene expression interactions, and how they relate to our approach.We find cell state-dependent eQTLs and co-expression QTLs to be remarkably synonymous, and have added a section to the manuscript describing their relationship.We believe this will provide valuable clarity, and we are grateful for the recommendation!updated Main text: There are multiple ways to represent how eQTL effects depend on transcriptional context.We use dimensionality reduction to identify major transcriptional gradients that each approximate a cell state such as T reg .We then identify cell-state-dependent eQTLs, where a genotype's effect on a gene's expression is modulated by the transcriptional gradient 5 .Because transcriptional gradients may be tagged by key marker genes, however, it is sometimes possible to reframe cell-state-dependent eQTLs as co-expression QTLs (co-eQTLs) 20,21 .In the co-eQTL framing, the correlation in expression between gene A and gene B depends on a genotype.An alternative way to describe this phenomenon is that the genotype's effect on gene A depends on the expression of gene B. If gene B is expressed in a specific cell state, cell-state-dependent eQTL and co-eQTL are synonymous terms.If gene B does not tag a cell state, the locus is not a cell-state-dependent eQTL, but would still be considered a co-eQTL.The co-eQTL framework, therefore, offers a more inclusive interpretation of gene expression interaction.The vast number of possible pairings between genes and their regulatory loci precludes comprehensive detection of co-eQTLs, which would be required to estimate the proportion of eQTLs that are co-eQTLs.Alternatively, by focusing on cell states rather than individual genes, we are able to estimate that approximately one third (33%) of eQTLs in T cells depend on transcriptional context 5 .

new Figure 2 .Figure 2 .Reviewer # 1 Comment 2 . 1
Figure2.Schematic illustrating molecular phenotypes that could be affected by a hypothetical quantitative trait locus (QTL).At this hypothetical locus, an individual may have a TT genotype (in purple), an AA genotype (in orange), or be heterozygous.Along the top row, we see molecular consequences of genotype TT.Along the bottom row, we see molecular consequences of genotype AA.We depict six types of QTLs as examples; this set of six is not comprehensive.

new Figure 2 .Figure 2 .
Figure2.Schematic illustrating molecular phenotypes that could be affected by a hypothetical quantitative trait locus (QTL).At this hypothetical locus, an individual may have a TT genotype (in purple), an AA genotype (in orange), or be heterozygous.Along the top row, we see molecular consequences of genotype TT.Along the bottom row, we see molecular consequences of genotype AA.We depict six types of QTLs as examples; this set of six is not comprehensive.

Table 1 .
table will provide a more practical guide for those interested in beginning research in this area.Useful resources for the study and interpretation of immune cell QTLs My suggestion would be to expand the discussion around the apparent lack of co-localization between eQTLs and GWAS variants (this seems very relevant, if the goal is to functionally interpret GWAS variants; is the co-localization better in single cell datasets from patients' tissue?)